{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a3b44703",
   "metadata": {},
   "source": [
    "# Regression: predict fuel efficiency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "416b0a4b",
   "metadata": {},
   "source": [
    "Copyright 2016 The BigDL Authors."
   ]
  },
  {
   "cell_type": "raw",
   "id": "a474a628",
   "metadata": {},
   "source": [
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf304c91",
   "metadata": {},
   "source": [
    "SparkXshards in Orca allows users to process large-scale dataset using existing Python codes in a distributed and data-parallel fashion, as shown below. This notebook is an example of comprehensive data exploration using SparkXshards on Orca. \n",
    "\n",
    "It is adapted from [TensorFlow tutorial of basic regression] (https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/keras/basic_regression.ipynb) on auto data. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa7ba935",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use seaborn for some plots\n",
    "!pip install seaborn pandas numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fca5adf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary libraries\n",
    "import bigdl.orca.data.pandas\n",
    "from bigdl.orca import init_orca_context, stop_orca_context\n",
    "from bigdl.orca.data.transformer import *\n",
    "from bigdl.orca.learn.tf2.estimator import Estimator\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import math\n",
    "\n",
    "import tensorflow.compat.v1 as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "833f9193",
   "metadata": {},
   "source": [
    "Start an OrcaContext and setup backend using \"pandas\", then it will use pandas to read each file into a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cb05ce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = init_orca_context(memory=\"4g\")\n",
    "OrcaContext.pandas_read_backend = \"pandas\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a46ad6a7",
   "metadata": {},
   "source": [
    "##  Load data in parallel and get general information"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "904060e4",
   "metadata": {},
   "source": [
    "Load data into data_shards, it is a SparkXshards that can be operated on in parallel, here each element of the data_shards is a panda dataframe read from a file on the cluster. Users can distribute local code of `pd.read_csv(dataFile)` using `bigdl.orca.data.pandas.read_csv(datapath)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7be0d69e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "datapath = '../auto-mpg.csv'\n",
    "column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',\n",
    "                'Acceleration', 'Model Year', 'Origin']\n",
    "data_shards = bigdl.orca.data.pandas.read_csv(file_path=datapath, names=column_names,\n",
    "                                              na_values=\"?\", comment='\\t',\n",
    "                                              sep=\" \", skipinitialspace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d12d912",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "0  18.0          8         307.0       130.0  3504.0          12.0   \n",
       "1  15.0          8         350.0       165.0  3693.0          11.5   \n",
       "2  18.0          8         318.0       150.0  3436.0          11.0   \n",
       "3  16.0          8         304.0       150.0  3433.0          12.0   \n",
       "4  17.0          8         302.0       140.0  3449.0          10.5   \n",
       "\n",
       "   Model Year  Origin  \n",
       "0          70       1  \n",
       "1          70       1  \n",
       "2          70       1  \n",
       "3          70       1  \n",
       "4          70       1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the first couple of rows in the data_shards\n",
    "print(data_shards.head(5))\n",
    "print(data_shards._get_class_name())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62d45e04",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see the num of partitions of data_shards\n",
    "data_shards.num_partitions()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b8526d9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "398"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count total number of rows in the data_shards\n",
    "len(data_shards)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce183207",
   "metadata": {},
   "source": [
    "## Clean the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef28781d",
   "metadata": {},
   "source": [
    "The dataset contains a few unknown values, drop them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "abbc976d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horsepower</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MPG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cylinders</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Displacement</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Weight</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            col  total\n",
       "3    Horsepower      6\n",
       "0           MPG      0\n",
       "1     Cylinders      0\n",
       "2  Displacement      0\n",
       "4        Weight      0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_cnt_pdf = data_shards.get_null_sum().sort_values(by=\"total\", ascending=False)\n",
    "null_cnt_pdf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1f93fc66",
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_na(df):\n",
    "    df = df.dropna()\n",
    "    return df\n",
    "data_shards = data_shards.transform_shard(drop_na)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "121255d2",
   "metadata": {},
   "source": [
    "The \"Origin\" column is really categorical, not numeric. So convert that to a one-hot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6075bcb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_extra_cols(df):\n",
    "    origin = df.pop('Origin')\n",
    "    df['USA'] = (origin == 1)*1.0\n",
    "    df['Europe'] = (origin == 2)*1.0\n",
    "    df['Japan'] = (origin == 3)*1.0\n",
    "    return df\n",
    "\n",
    "data_shards = data_shards.transform_shard(generate_extra_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f8598d",
   "metadata": {},
   "source": [
    "## Inspect the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c76ffc7",
   "metadata": {},
   "source": [
    "Users can compute the descriptive statistics summary of on the full data_shards in a distributed way. For local visualization, users can sample a portion of the data_shards across the cluster into a local panda dataframe, then analyze it using traditional tools like seaborn for plots."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dc83601",
   "metadata": {},
   "source": [
    "###  Local visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93a246d7",
   "metadata": {},
   "source": [
    "Users can call `data_shards.sample_to_pdf(frac)` to sample a portion of the data_shards and bring back to a panda dataframe on local for visualization. \n",
    "Just a few examples are shown in here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c9c7c5c4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 0 < frac <= 1, here frac = 1.0 is chosen to collect and show full data.\n",
    "pdf = data_shards.sample_to_pdf(frac = 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5a39cf2f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(pdf[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24713dbf",
   "metadata": {},
   "source": [
    "Plot scatter plots to show relationship with numerical variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a3ba2a",
   "metadata": {},
   "source": [
    "## Normalize the data using MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0c1c4b63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight', 'Acceleration', 'Model Year', 'USA', 'Europe', 'Japan']\n",
      "createDataFrame from shards attempted Arrow optimization failed as: 'NoneType' object has no attribute 'json',Will try without Arrow optimization\n",
      "create shards from Spark DataFrame attempted Arrow optimization failed as: name 'df' is not defined. Will try without Arrow optimization\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "columns = data_shards.get_schema()['columns']\n",
    "print(columns)\n",
    "scaler = MinMaxScaler(inputCol=list(columns[1:]), outputCol=\"scaled_vec\")\n",
    "data_shards = scaler.fit_transform(data_shards)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06ca17ff",
   "metadata": {},
   "source": [
    "## Train validation split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d1568364",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test(df):\n",
    "    train_df = df.sample(frac=0.8, random_state=0)\n",
    "    test_df = df.drop(train_df.index)\n",
    "    return train_df, test_df\n",
    "\n",
    "shards_train, shards_val = data_shards.transform_shard(split_train_test).split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "95f8dd5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>USA</th>\n",
       "      <th>Europe</th>\n",
       "      <th>Japan</th>\n",
       "      <th>081c9e2e-6871-11ed-aba1-dca9048df0d4x_vec</th>\n",
       "      <th>scaled_vec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>90.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>74</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[4.0, 90.0, 75.0, 2125.0, 14.5, 74.0, 1.0, 0.0...</td>\n",
       "      <td>[0.20000000298023224, 0.05684754624962807, 0.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>22.3</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2890.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>79</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[4.0, 140.0, 88.0, 2890.0, 17.299999237060547,...</td>\n",
       "      <td>[0.20000000298023224, 0.1860465109348297, 0.22...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>12.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>4456.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>72</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[8.0, 350.0, 160.0, 4456.0, 13.5, 72.0, 1.0, 0...</td>\n",
       "      <td>[1.0, 0.7286821603775024, 0.6195651888847351, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>38.0</td>\n",
       "      <td>4</td>\n",
       "      <td>105.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[4.0, 105.0, 63.0, 2125.0, 14.699999809265137,...</td>\n",
       "      <td>[0.20000000298023224, 0.09560723602771759, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>33.8</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2145.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[4.0, 97.0, 67.0, 2145.0, 18.0, 80.0, 0.0, 0.0...</td>\n",
       "      <td>[0.20000000298023224, 0.07493539899587631, 0.1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "144  28.0          4          90.0        75.0  2125.0          14.5   \n",
       "280  22.3          4         140.0        88.0  2890.0          17.3   \n",
       "68   12.0          8         350.0       160.0  4456.0          13.5   \n",
       "372  38.0          4         105.0        63.0  2125.0          14.7   \n",
       "328  33.8          4          97.0        67.0  2145.0          18.0   \n",
       "\n",
       "     Model Year  USA  Europe  Japan  \\\n",
       "144          74  1.0     0.0    0.0   \n",
       "280          79  1.0     0.0    0.0   \n",
       "68           72  1.0     0.0    0.0   \n",
       "372          82  1.0     0.0    0.0   \n",
       "328          80  0.0     0.0    1.0   \n",
       "\n",
       "             081c9e2e-6871-11ed-aba1-dca9048df0d4x_vec  \\\n",
       "144  [4.0, 90.0, 75.0, 2125.0, 14.5, 74.0, 1.0, 0.0...   \n",
       "280  [4.0, 140.0, 88.0, 2890.0, 17.299999237060547,...   \n",
       "68   [8.0, 350.0, 160.0, 4456.0, 13.5, 72.0, 1.0, 0...   \n",
       "372  [4.0, 105.0, 63.0, 2125.0, 14.699999809265137,...   \n",
       "328  [4.0, 97.0, 67.0, 2145.0, 18.0, 80.0, 0.0, 0.0...   \n",
       "\n",
       "                                            scaled_vec  \n",
       "144  [0.20000000298023224, 0.05684754624962807, 0.1...  \n",
       "280  [0.20000000298023224, 0.1860465109348297, 0.22...  \n",
       "68   [1.0, 0.7286821603775024, 0.6195651888847351, ...  \n",
       "372  [0.20000000298023224, 0.09560723602771759, 0.0...  \n",
       "328  [0.20000000298023224, 0.07493539899587631, 0.1...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shards_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3479a1be",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "314 78\n"
     ]
    }
   ],
   "source": [
    "print(len(shards_train), len(shards_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c38da308",
   "metadata": {},
   "source": [
    "## The model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3806b914",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "Build the model model as usual. Here, we'll use a Sequential model with two densely connected hidden layers, and an output layer that returns a single, continuous value. The model building steps are wrapped in a function, build_model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "394393c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(config):\n",
    "    model = Sequential([\n",
    "        Dense(64, activation=tf.nn.relu, input_shape=[9]),\n",
    "        Dense(64, activation=tf.nn.relu),\n",
    "        Dense(1)\n",
    "    ])\n",
    "\n",
    "    optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
    "\n",
    "    model.compile(loss='mean_squared_error',\n",
    "                  optimizer=optimizer,\n",
    "                  metrics=['mean_absolute_error', 'mean_squared_error'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5ff9aecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "est = Estimator.from_keras(model_creator=build_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52c640cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 1000\n",
    "batch_size = 16\n",
    "train_steps = math.ceil(len(shards_train) / batch_size)\n",
    "est.fit(data=shards_train,\n",
    "        batch_size=batch_size,\n",
    "        epochs=EPOCHS,\n",
    "        steps_per_epoch=train_steps,\n",
    "        feature_cols=['scaled_vec'],\n",
    "        label_cols=['MPG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d9c18a3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stopping orca context\n"
     ]
    }
   ],
   "source": [
    "stop_orca_context()"
   ]
  }
 ],
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